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gortex

Code-intelligence engine in one static Go binary: tree-sitter graph over 257 languages, compiler-grade resolution for 17, multi-repo, 175 configurable MCP tools — up to 50x fewer tokens. 100% local.

1,071 98 Go Apache-2.0updated yesterday

code-intel local

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Curator's take

The coverage play on this shelf: 257 grammars, cross-repo and cross-SERVICE edges (HTTP routes, contracts) with a provenance/confidence tier the single-repo tools don't attempt, and one install that configures 19 coding agents. When NOT: 175 MCP tools is a context bill of its own — prune the toolset or your agent pays the index's price in schema tokens; benchmarks are self-published; and if you want memory as plain files, this is the opposite philosophy.

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README.md

Gortex

High-performance and efficient code-intelligence engine for AI agents and IDE

Indexes code into graph and exposes it via CLI, MCP Server, and web UI. Multi-repository support by default.

Single static binary for macOS, Linux, and Windows — no dependency chain, simple installation and use.

High-quality parsing 257 languages/grammars through tree-sitter AST analysis, in-process resolvers, enhanced with compiler-grade resolution for Python, TypeScript / JavaScript, PHP, C#, Go, C, C++, Java, Kotlin, Swift, Zig, Rust, Ruby, Elixir, Ocaml, Haskell, and others - producing a persistent provenance-tiered knowledge graph of functions, classes, call chains, HTTP routes, and cross-service contracts and calls with a strong confidence model. 175 (configurable) MCP tools - use only what you need. Zero dependencies. Plug and play across 19 coding agents. Up to 50× fewer tokens per response. Reproducible benchmarks.

19 AI coding agents (Claude Code, Kiro, Cursor, Windsurf, VS Code / Copilot, Continue.dev, Cline, OpenCode, Antigravity, Codex CLI, Gemini CLI, Zed, Aider, Kilo Code, OpenClaw, Hermes, Oh My Pi, Pi, Kimi) supported out of the box.

One install configures every one detected on your machine — see docs/agents.md.

Gortex Web UI — force-directed knowledge graph visualization

Why it matters

50× fewer tokens per response — graph-native lookups beat naive file reads. Agents read just what they need, not the full file, not the 500-line file around it.
Full development cycle - no 'read file before edit'. Agents ask for sources, tell what to change, and don't waste context of reading noise.
257 languages/grammars - every file in the repository reachable; no mix of tools and bloating/hallycinating agents. Three tiers (bespoke tree-sitter, regex, forest-backed signatures) plus Jupyter and Databricks notebooks → docs/languages.md
Cross-repo by default — N repos in one graph; contracts, references, and call chai

Continue your stack

What teams reach for next — and why each earns a place beside gortex. Ranked by curator confidence.

Alternative to See all + compare →

codebase-memory-mcp

Code intelligence MCP in pure C: tree-sitter knowledge graph over 158 languages, average repo indexed in milliseconds, sub-ms queries, 10x fewer tokens. Single static binary, zero deps.

Why it fitsThe closest fight on the shelf: both are single-binary tree-sitter knowledge graphs served over MCP. codebase-memory-mcp bets on C-core minimalism; gortex on language breadth and cross-service resolution.

tokensave

Code-intelligence MCP server for coding agents — a pre-indexed semantic graph (libSQL + FTS5) they query instead of grepping: symbols, callers, impact radius in one call. 100% local, 50+ languages.

Why it fitsSame pitch — a pre-indexed semantic graph agents query instead of grepping. tokensave is the lean libSQL take; gortex scales the same idea to multi-repo and 257 languages.

codegraph-mcp

No-train, on-prem code knowledge graph served to AI agents over MCP — symbols, call edges, cross-language links and blast-radius queries, with a hash-chained audit log of every read.

Why it fitsBoth serve on-prem code graphs with call edges and cross-language links over MCP; codegraph-mcp differentiates on the hash-chained audit log, gortex on coverage and agent integrations.